Overcoming the Lack of Decimal Precision in SQLite

2024-07-27

The data type you declare for the column (e.g., INTEGER, REAL) acts as a suggestion, not a strict rule.
SQLite analyzes the value being inserted and assigns it an appropriate affinity.

For numeric values, SQLite typically uses either INTEGER or REAL affinity.

INTEGER: Stores whole numbers (no decimals).
REAL: Stores numbers using a 64-bit IEEE 754 floating-point format, which offers around 15-16 digits of precision.

This means you can't enforce a specific decimal precision in SQLite at the table definition level.

However, there are ways to work around this limitation:

Example Codes for Handling Decimal Precision in SQLite

Storing Numbers as Text:

CREATE TABLE products (
  id INTEGER PRIMARY KEY,
  name TEXT,
  price TEXT
);

INSERT INTO products (name, price)
VALUES ('Widget', '12.3456');

SELECT * FROM products;

This code creates a table with an id (integer), name (text), and price (text) column. Even though price is declared as text, you can insert decimal values as strings.

Using the Round Function for Displaying Decimals:

SELECT id, name, ROUND(CAST(price AS REAL), 2) AS formatted_price
FROM products;

This code retrieves data from the products table. We use the CAST function to convert the price (text) to a REAL number for calculations. Then, we use the ROUND function with two arguments:

The expression to round (CAST(price AS REAL))
The number of decimal places to keep (2 in this case)

This allows you to display the price with two decimal places even though it's stored as text.

Note:

Keep in mind that storing numbers as text prevents calculations within SQLite. You'll need to convert them back to REAL for operations.

Leveraging Constraints:

While you can't define column precision directly, you can use check constraints to define limitations on the values stored. This approach doesn't enforce precision but helps maintain data integrity within a specific range.

Here's an example:

CREATE TABLE products (
  id INTEGER PRIMARY KEY,
  name TEXT,
  price REAL CHECK (price >= 0.00 AND price < 9999.99)
);

This code creates a table with a price column restricted to values between 0.00 and 9999.99 (you can adjust the range as needed). This doesn't guarantee two decimal places, but it ensures values represent prices within a reasonable range.

Post-processing with Application Logic:

You can leverage your application logic to handle decimal precision after data retrieval. Here's the idea:

Store numeric values as REAL (or INTEGER if decimals aren't needed).
When retrieving data from the database, use your application code to format the retrieved values according to your desired precision using functions like round or string formatting.

This approach allows flexibility as you can define the formatting logic within your application independent of the database.

Third-party Libraries (for specific languages):

Some programming languages might offer libraries or frameworks that interact with SQLite and provide functionalities for handling decimal precision. These libraries might offer functions to interpret or format numeric data retrieved from SQLite.

Choosing the Right Method:

The best method depends on your specific needs. Here's a quick guide:

Use constraints if you need to enforce a specific value range but don't require strict decimal places.
Post-process with application logic if you need flexibility in formatting and calculations on retrieved data.
Consider third-party libraries if your programming language offers tools specifically designed for handling decimals with SQLite.

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