Summary: The Postgres DECIMAL and NUMERIC types store exact numeric values using user-defined precision and scale. They are the right choice for financial and scientific data where floating-point rounding errors are unacceptable. Specify DECIMAL(precision, scale) to control digit count and fractional places, or omit both to store any precision exactly up to the implementation limit. Unlike REAL or DOUBLE PRECISION, DECIMAL/NUMERIC arithmetic never approximates, though exact-precision calculations are slower than floating-point operations.

Work with exact numerical values in Postgres

In Postgres, decimal data types are used to represent numbers with arbitrarily high precision. They are crucial in financial applications and scientific computation, where exact precision is required for numerical calculations.

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Postgres provides a single decimal/numeric type referred to as DECIMAL or NUMERIC. It offers user-defined precision and can represent numbers exactly up to a certain number of digits.

The syntax for defining a decimal column is DECIMAL(precision, scale) or NUMERIC(precision, scale), where:

• precision is the total count of significant digits in the number (both to the left and right of the decimal point).
• scale is the count of decimal digits in the fractional part.

Declaring a column as NUMERIC without specifying precision and scale, stores numbers of any precision exactly (up to the implementation limit).

We illustrate the behavior of NUMERIC with the following example:

SELECT 1234.56::NUMERIC(10, 4) AS num_A,
       1234.56::NUMERIC(10, 1) AS num_B,
       1234.56789::NUMERIC AS num_C;

This query yields the following output:

num_a     | num_b  |   num_c
----------+--------+------------
1234.5600 | 1234.6 | 1234.56789

The number 1234.56 is represented exactly in all three cases. However, the NUMERIC(10, 4) type rounds the number to 4 decimal places, while NUMERIC(10, 1) rounds to 1 decimal place. When no precision and scale are specified, the number is stored exactly.

Consider a financial application managing user portfolios. Here, DECIMAL is ideal for storing currency values to avoid rounding errors. For example, representing the price of a stock or the total value of a portfolio.

The following SQL creates a portfolios table:

CREATE TABLE portfolios (
    portfolio_id SERIAL PRIMARY KEY,
    user_id INTEGER NOT NULL,
    stock_symbol TEXT NOT NULL,
    shares_owned DECIMAL(10, 4),
    price_per_share DECIMAL(10, 2)
);

INSERT INTO portfolios (user_id, stock_symbol, shares_owned, price_per_share)
VALUES
    (101, 'AAPL', 150.1234, 145.67),
    (102, 'MSFT', 200.000, 214.53);

Postgres allows various arithmetic operations on decimal types. These operations maintain precision and are critical in contexts where rounding errors could be costly.

For example, the following query calculates the total value of each stock holding:

SELECT price_per_share * shares_owned AS total_value
FROM portfolios;

This query yields the following output:

total_value
--------------
21868.475678
42906.000000

This query calculates the total value of each stock holding with precise decimal representation.

It's important to differentiate DECIMAL/NUMERIC from floating-point types (REAL, DOUBLE PRECISION):

• Precision: DECIMAL/NUMERIC types maintain exact precision, while floating-point types are approximate and can introduce rounding errors.
• Performance: Operations on DECIMAL/NUMERIC types are generally slower than floating-point types due to the precision and complexity of calculations.

• Range and Precision: Always define DECIMAL/NUMERIC with an appropriate range and precision based on the application's requirements. Overestimating precision can lead to unnecessary storage and performance overhead.

• PostgreSQL documentation - Numeric Types

• Array
• Boolean
• Date and time
• Character
• JSON
• Floating point
• Integer
• Tsvector
• UUID

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