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When Should You Use `var` in C# for Type Inference?

Barbara Streisand
Barbara StreisandOriginal
2025-02-02 05:51:09259browse

When Should You Use `var` in C# for Type Inference?

C# type Inference

The correct usage of var

Introduction:

C#

Keywords allow type inferences, and the compiler determines the type of variable based on the initialization of the variable. Although it can improve the readability of code, it also arouses concerns about type security. This article discusses the appropriate usage of and discusses potential traps.

var Reasonable usage: var

When the intention is clear: assign the new object to the variable, such as

or
    .
  • Tyrd type name: Replace the lengthy and complex type names, such as var l = new List<string>(); instead of var s = new SomeClass();.
  • Potential question: var results = from r in dataContext.SomeTable select r; var results = from r in dataContext.SomeTable select new { r.Id, r.Name };
  • Type Vagueness:
When traversing a collection or using Linq query,

may cause ambiguity, because the exact type of variables is not immediately clear. Research problem:

In accepting multiple compatible types of heavy load methods, using
    may cause the type of error inadvertently, because the compiler may not make an error.
  • Suggestion: var
  • Despite some concerns, can still improve the readability of the code in some cases. However, when dealing with sets, linq queries, or overload methods, should be used with caution. It is recommended to avoid the use of var in these cases to give priority to type safety and clarity.
Example:

Consider the following example:

var It is reasonable to use here, because the purpose of variables is to store order collection, regardless of its specific types. Type security is maintained because it still checks the type of type based on the type of 'Orders' Properties. var var Conclusion:

Although provides convenience, it needs to be used with caution to maintain type safety and avoid ambiguity. When using in C# code, the benefits and potential risks of weighing readability are essential.

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