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Discover how to resolve rounding errors in your Snowflake calculations with simple adjustments to your operations.
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How to Fix Rounding Errors in Snowflake Calculations

Rounding errors in data calculations can be frustrating, especially when you're trying to achieve precise results. One common occurrence is when using the Snowflake cloud data platform, where arithmetic operations may yield unexpected answers due to how numbers are rounded. In this post, we'll explore why these rounding errors happen in Snowflake and how you can fix them to get the correct calculations.

The Problem with Rounding Errors

Let's take a look at a practical example that highlights the issue. When performing the following calculation in Snowflake:

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The result you receive is 84186921, which is not the expected 84186926. So, why does this rounding issue occur?

Understanding Floating Point Numbers

First, it's important to grasp what floating point numbers are. When you divide 84186926 by 91.0 in a standard calculator, you'll get:

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Multiplying this by 91 gives you the expected result:

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However, in Snowflake, the default behavior for numeric calculations isn't as straightforward. Here’s what happens step by step in Snowflake:

Default Precision: Snowflake defaults to 6 decimal places. Therefore, 84186926 / 91.0 results in:

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Rounding: The ROUND() function defaults to zero decimal places, hence it rounds:

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Final Multiplication: Finally, when you multiply 925131 by 91.0, you get:

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Key Insight: Order of Operations Matters

If you shift the ordering of operations in the calculation, like so:

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You might think this would yield 84186926, but it actually reduces the precision because you are getting 84186925.9..., which rounds down.

Recommended Solutions to Fix Rounding Errors

To avoid these rounding errors, here are a few strategies you can apply:

1. Change the Order of Operations

Rather than dividing first, you might rearrange your calculations. For example, maintaining your original number and performing multiplications before division will help retain precision where needed.

2. Use Higher Precision

You can specify a higher precision for your numeric operations. For instance, use the number data type with a defined precision:

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This gives a more precise result:

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It’s crucial to keep the order of operations in mind to avoid any errors related to precision loss during division.

Conclusion

Rounding errors are common pitfalls when performing calculations in Snowflake, primarily due to default numeric precision settings. By understanding how floating point numbers work and adopting better practices for operation ordering and precision, you can effectively resolve these issues. Whether you choose to change the order of your calculations or utilize fixed precision formats, these strategies will help you obtain accurate results in your data operations.

By following these guidelines, you'll be able to avoid the rounding errors that can lead to unexpected outcomes in your calculations. Happy querying in Snowflake!