Handling NaN Values in Dapper Queries with PostgreSQL

Learn how to deal with `NaN` values when using Dapper with PostgreSQL and avoid unexpected exceptions in your queries!
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Handling NaN Values in Dapper Queries with PostgreSQL

When working with data in a PostgreSQL database, you may encounter challenges due to the presence of NaN (Not a Number) values in numeric columns. This can lead to exceptions when querying data with Dapper, a .NET micro ORM, particularly when using the C# double type. In this guide, we will explore the issue, the root cause of the exception you're facing, and how to effectively resolve it.

The Problem

Many developers use PostgreSQL's Numeric type to store decimal values, which allows for NaN effectively. However, when trying to query such a table through Dapper, you may run into the following exception:

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Example Case

Consider the following table schema:

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And here's how you might query this data using Dapper:

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The issue arises because the Dapper library may attempt to set the double property of your class to NaN, causing conflicts with its handling of types. This specific case leads to the unexpected exception in your application.

Understanding the Cause

PostgreSQL's Numeric type corresponds closely to the C# decimal type, which does not accommodate NaN values. Consequently, Dapper may inadvertently interpret the NaN value as an invalid decimal, causing the cascading exception and breaking your application flow.

Solution Options

To mitigate the issue with NaN values when using Dapper to query PostgreSQL, you have a couple of viable solutions:

1. Modify the Database Schema

The cleanest approach is to modify the data type in the database from Numeric to double using either DOUBLE PRECISION or REAL. This change will allow you to avoid the NaN conflict altogether since these types can handle NaN properly.

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2. Cast Values in Your Queries

If altering the database schema is not feasible, you can work around the issue by explicitly casting the values to DOUBLE PRECISION in your query. This way, Dapper will not misinterpret the NaN values because you are providing the data in a compatible format.

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Summary

Handling NaN values in PostgreSQL when querying with Dapper can present some unique challenges due to type mismatches. By understanding the underlying cause of the Unknown message code: 0 exception and implementing one of the suggested solutions—either modifying the schema to use DOUBLE PRECISION/REAL or explicitly casting in your queries—you can effectively resolve the issue and ensure smoother data interactions within your applications.

Embrace these strategies to enhance your Dapper and PostgreSQL experience, ensuring that data integrity and performance are maintained as you scale your projects!

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