When to Use Double Precision and Its Alternatives in PostgreSQL

Double Precision in PostgreSQL

• Inexactness
  It's important to note that double precision, like most floating-point data types, is inexact. This means that certain real numbers cannot be stored precisely due to limitations in how computers represent numbers internally. These limitations can lead to slight rounding errors during calculations.
• Range
  Double precision boasts a wider range of representable values, typically from approximately -1.7976931348623157E+308 to +1.7976931348623157E+308. This allows it to handle a broader spectrum of numerical data.
• Precision
  It offers a higher level of precision compared to single precision. Double precision can represent numbers with at least 15 decimal digits accurately, while single precision is limited to around 6 decimal digits.
• Storage
  Double precision uses 8 bytes of storage space, making it more spacious than its counterpart, REAL (single precision), which uses 4 bytes.

Choosing Between Double Precision and Other Data Types

When selecting a data type for numerical values in PostgreSQL, consider these factors:

• Exact Calculations
  If you require exact decimal representation, especially for financial calculations, consider using the NUMERIC or DECIMAL data types. These data types store numbers with a fixed number of digits before and after the decimal point, ensuring exactness.
• Storage Efficiency
  For situations where storage space is a premium and high precision isn't critical, single precision (REAL) might be a better choice due to its smaller size.
• Range Requirements
  If your data spans a vast range of values, double precision offers more flexibility.
• Precision Requirements
  If you need very high precision (more than 6 decimal digits), double precision is the way to go.

Example

CREATE TABLE measurements (
  id SERIAL PRIMARY KEY,
  value DOUBLE PRECISION NOT NULL
);

This table would store values with decimal parts, offering a balance between precision and storage efficiency for most scientific or engineering applications.

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Creating a Table with a Double Precision Column

CREATE TABLE product_prices (
  id SERIAL PRIMARY KEY,
  product_name VARCHAR(255) NOT NULL,
  unit_price DOUBLE PRECISION NOT NULL,
  discount DECIMAL(5,2)  -- Example of using DECIMAL for exact monetary values
);

This code creates a table product_prices with three columns:

• discount: Stores the discount percentage as a decimal number with two decimal places (using DECIMAL for exactness)
• unit_price: Stores the unit price of the product as a double precision number, allowing for decimal parts
• product_name: Stores the product name as text (up to 255 characters)
• id: An auto-incrementing serial number (primary key)

Inserting Data with Double Precision Values

INSERT INTO product_prices (product_name, unit_price, discount)
VALUES ('T-Shirt', 19.99, 0.10);  -- 10% discount

This code inserts a new row into the product_prices table with:

• discount: 0.10 (decimal)
• unit_price: 19.99 (double precision)
• product_name: "T-Shirt"

Performing Calculations with Double Precision

SELECT product_name, unit_price * (1 - discount) AS discounted_price
FROM product_prices;

This code retrieves data from the product_prices table and calculates the discounted price for each product:

• The result is displayed in a new column named discounted_price.
• It multiplies the unit_price by (1 minus the discount) to get the discounted price. This calculation uses double precision for the intermediate result.
• It selects the product_name and unit_price.

Using Functions with Double Precision

SELECT product_name, unit_price, SQRT(unit_price) AS square_root
FROM product_prices;

This code uses the SQRT function (square root) on the unit_price column, which is a double precision value. The result, also a double precision number, is displayed in a new column named square_root.

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REAL (Single Precision)

• Considerations
  Be aware of the lower precision limit. Calculations involving large numbers or many decimal places might lead to rounding errors.
• Use Case
  If you need to store numbers with decimal parts but don't require the high precision of double precision (around 6 decimal digits of accuracy) and storage space is a concern, REAL (single precision) is a good option. It uses 4 bytes of storage compared to double precision's 8 bytes.

NUMERIC (or DECIMAL)

• Considerations
  Specify the precision (total number of digits) and scale (number of digits after the decimal point) during column creation to suit your data requirements. NUMERIC and DECIMAL are synonyms in PostgreSQL.
• Use Case
  When exact decimal representation is crucial, especially for financial calculations, NUMERIC or DECIMAL are the preferred choices. These data types store numbers with a fixed number of digits before and after the decimal point, ensuring precise calculations.

Other Numeric Types

• Considerations
  Choose the appropriate type based on the range of numbers you anticipate.
• Use Case
  These are efficient for storing quantities, identifiers, or other whole number values.
• SMALLINT, INTEGER, BIGINT
  If you only need to store whole numbers (no decimal parts) within a specific range, consider these integer types:
  • SMALLINT: -32,768 to 32,767 (2 bytes)
  • INTEGER: -2,147,483,648 to 2,147,483,647 (4 bytes)
  • BIGINT: Wider range for very large whole numbers (8 bytes)

Choosing the Right Alternative

The best alternative depends on your specific requirements:

• Range
  For whole numbers within a specific range, choose the appropriate integer type (SMALLINT, INTEGER, BIGINT).
• Exactness
  If absolutely precise decimal representation is essential, use NUMERIC or DECIMAL.
• Storage
  For situations where storage space is a significant factor, consider REAL (single precision) if your precision needs are lower.
• Precision
  If high precision (more than 6 decimal digits) is necessary, stick with double precision.