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May 31, 2023

Building a dynamic logical expression builder in C#

Автор:
Yohan Malshika
Источник:
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Understanding uow to use And and Or operators with Expression Trees

Building a Dynamic Logical Expression Builder in C#

As a C# developer, you may have come across scenarios where you need to build complex logical expressions dynamically based on user input or other dynamic factors. In such cases, building expressions statically can become tedious and error-prone.

In this article, we’ll explore how to use expression trees to build logical expressions in C#. We’ll start with a simple example that combines multiple conditions using the And and Or operators. Then, we'll look at how we can prioritize the And and Or operators to create more efficient expressions.

Our example scenario involves a list of boolean values, and a set of logic requests. Each logic request specifies a boolean value and a logical operator (either “AND” or “OR”). Our goal is to build a logical expression that applies these operators to the boolean values in the list.

Before moving to the example, Let’s understand what is Expression Trees in C#.

Expression Trees in C#

Expression Trees are a powerful feature in C# that allow developers to represent code as data structures. In essence, expression trees allow you to build code as a tree-like structure, where each node in the tree represents a piece of code or an operation.

Expression Trees are useful in scenarios where you need to dynamically build and execute code at runtime, such as in query languages or in code that needs to be generated on-the-fly. They can also be used for code analysis, optimization, and transformation.

Advantages of Expression Trees:

  1. Dynamic code generation: With expression trees, you can build code at runtime, making it possible to create dynamic queries, database operations, and other types of code that require dynamic generation.
  2. Code analysis: Expression trees can be used to analyze code, identify patterns, and make optimizations based on that analysis.
  3. Transformation: Expression trees can be used to transform code, making it possible to change code from one form to another, such as from a query to SQL.
  4. Strongly-typed code: Expression trees can be strongly-typed, which means that the compiler can verify that the code you’re generating is valid before it’s executed.
  5. Familiar syntax: The syntax used to create expression trees is very similar to the syntax used in C# code, making it easy for developers to learn and use.

Disadvantages of Expression Trees:

  1. Complexity: Expression trees can be quite complex, especially when dealing with nested expressions, which can make them harder to understand and debug.
  2. Performance: Generating and executing code at runtime can have performance implications, especially if the code needs to be generated and executed frequently.
  3. Learning curve: Although the syntax used to create expression trees is similar to C# syntax, it can still be challenging for developers who are not familiar with the concept.

To accomplish this, we’ll make use of the System.Linq.Expressions namespace, which provides a way to represent code as data in the form of expression trees. Expression trees can be constructed dynamically at runtime, allowing us to build complex expressions programmatically.

Combining Multiple Conditions

Let’s start with a simple example. Suppose we have a list of LogicRequest objects that represent boolean conditions, along with a LogicType that specifies whether the conditions should be combined using And or Or. Our goal is to build a logical expression that evaluates to true if all the conditions are true (in the case of And) or if at least one condition is true (in the case of Or).

Here’s the code for the LogicRequest and LogicType classes:

public class LogicRequest
{
    public bool Result { get; set; }
    public LogicType Type { get; set; }
}

public enum LogicType
{
    And,
    Or
}

Then, we create a list of LogicRequest objects that specify whether each condition should be treated as an "AND" or "OR" condition. We create this list for this example. Then we can use this list as input to build expression.

var boolList = new List<bool>();

var logicRequests = new List<LogicRequest>();

logicRequests.Add(new LogicRequest()
{
    Result = true,
    Type = LogicType.And
});

logicRequests.Add(new LogicRequest()
{
    Result = false,
    Type = LogicType.Or
});

logicRequests.Add(new LogicRequest()
{
    Result = true,
    Type = LogicType.And
});

We’ll use these lists to build our expression dynamically. We’ll start by creating a list of ParameterExpressions, which represent the input parameters of our expression. In our case, we only have one input parameter, which is a list of boolean values.

var inputs = new List<ParameterExpression>();
var input = Expression.Parameter(typeof(List<bool>), "input");
inputs.Add(input);

Next, we’ll create a list of Expression objects that represent the boolean values in our list. We’ll use the MakeIndex method to access each value in the list by index, and the IsTrue method to convert each value to a boolean expression.

var results = new List<Expression>();

var count = 0;
foreach (var logicRequest in logicRequests)
{
    boolList.Add(logicRequest.Result);
    results.Add(Expression.IsTrue(Expression.MakeIndex(input, typeof(List<bool>).GetProperty("Item"), new[] { Expression.Constant(count) })));
    count++;
}

Now that we have our boolean expressions, we can build the logical expression by applying the logical operators specified in the logic requests. We’ll start by setting the initial condition to the first boolean expression in the list.

Expression condition = results[0];

Then, we’ll loop through the remaining boolean expressions and apply the appropriate logical operator based on the logic request.

for (int i = 1; i < results.Count; i++)
{
    if (logicRequests[i].Type == LogicType.And)
    {
        condition = Expression.AndAlso(condition, results[i]);
    }else if (logicRequests[i].Type == LogicType.Or)
    {
        condition = Expression.Or(condition, results[i]);
    }
}

Here, We start with the first condition, represented by the results[0] Expression object, and then loop through the remaining conditions. For each condition, we check whether it should be treated as an "AND" or "OR" condition, and then combine it with the previous conditions using the Expression.AndAlso or Expression.Or methods, respectively.

Finally, we create the if-else expression using the condition Expression object we created earlier, along with trueExpression and falseExpression objects that represent the true and false outcomes, respectively.

var trueExpression = Expression.Constant(true, typeof(bool));
var falseExpression = Expression.Constant(false, typeof(bool));
var ifElseExpression = Expression.Condition(condition, trueExpression, falseExpression);

We then compile the expression into a lambda function, and pass in the boolList object as the input parameter.

var lambda = Expression.Lambda<Func<List<bool>, bool>>(ifElseExpression, inputs).Compile();
var result = lambda(boolList);

Finally, we’ll create the if-else chain that returns the appropriate result using the simple example.

This code used for the simple example which including multiple conditions without prioritizing the conditons. When we dealing with multiple conditons, we have priortize them. Let’s discuss how overcome that challenge.

Prioritizing And and Or

In the previous section, we saw how to build a logical expression using Expression.AndAlso and Expression.OrElse. However, there may be cases where we want to prioritize certain operators over others. Here, we prioritizing the ‘And’ Condition by grouping them.

Let’s discuss the code for this example.

Expression condition = results[0];
var andConditions = new List<Expression>();
for (int i = 1; i < results.Count; i++)
{
    if (logicRequests[i].Type == LogicType.And)
    {
        andConditions.Add(results[i]);
    }
    else if (logicRequests[i].Type == LogicType.Or)
    {
        if (andConditions.Any())
        {
            var andCondition = andConditions[0];
            for (int j = 1; j < andConditions.Count; j++)
            {
                andCondition = Expression.AndAlso(andCondition, andConditions[j]);
            }
            condition = Expression.AndAlso(condition, andCondition);
            andConditions.Clear();
        }
        condition = Expression.OrElse(condition, results[i]);
    }
}

// Evaluate any remaining 'And' conditions
if (andConditions.Any())
{
    var andCondition = andConditions[0];
    for (int j = 1; j < andConditions.Count; j++)
    {
        andCondition = Expression.AndAlso(andCondition, andConditions[j]);
    }
    condition = Expression.AndAlso(condition, andCondition);
}

The code first checks each LogicRequest in the list to see if it is an And or an Or. If it is an And, the corresponding expression is added to a list of And conditions. If it is an Or, the code evaluates any remaining And conditions (if there are any) and adds them to the condition expression using Expression.AndAlso, followed by the Or expression using Expression.OrElse.

After processing all the LogicRequest objects, any remaining And conditions are evaluated in the same way as before.

This modified code gives priority to the And conditions over the Or conditions, ensuring that all And conditions are evaluated before any Or conditions are evaluated.

Conclusion

Expression Trees are a powerful feature in C# that allow you to build code dynamically and execute it at runtime. They can be used in a wide range of scenarios, such as dynamic queries, database operations, code analysis, optimization, and transformation. However, it’s important to keep in mind that expression trees can be complex, which can make them harder to understand and debug.

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