How to Measure Execution Time of a Method in C#

Measuring the execution time of C# methods is essential for performance optimization and identifying bottlenecks in your application.

The most straightforward approach uses the Stopwatch class from the System.Diagnostics namespace, which provides high-precision timing capabilities.

This approach is perfect for quick performance checks during development or when troubleshooting specific methods in production code.

Here's a practical example: Imagine you have a method that processes a large dataset and you want to measure its performance.

First, add using System.Diagnostics; to your imports. Then implement timing as shown below:

public void MeasurePerformance()
{
    Stopwatch stopwatch = new Stopwatch();
    
    // Start timing
    stopwatch.Start();
    
    // Call the method you want to measure
    ProcessLargeDataset();
    
    // Stop timing
    stopwatch.Stop();
    
    // Get the elapsed time
    Console.WriteLine($"Processing time: {stopwatch.ElapsedMilliseconds} ms");
    // Or use ElapsedTicks for higher precision
    Console.WriteLine($"Processing ticks: {stopwatch.ElapsedTicks}");
}

For more advanced scenarios, consider using the BenchmarkDotNet library, which offers comprehensive benchmarking with statistical analysis.

Simply install the NuGet package, decorate methods with the [Benchmark] attribute, and run BenchmarkRunner.Run<YourBenchmarkClass>() to generate detailed reports comparing different implementation strategies.

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Related

Slow initial load times can drive users away from your React application. One powerful technique to improve performance is lazy loading - loading components only when they're needed.

Let's explore how to implement this in React.

The Problem with Eager Loading

By default, React bundles all your components together, forcing users to download everything upfront. This makes navigation much quicker and more streamlined once this initial download is complete.

However, depending on the size of your application, it could also create a long initial load time.

import HeavyComponent from './HeavyComponent';
import AnotherHeavyComponent from './AnotherHeavyComponent';

function App() {
  return (
    <div>
      {/* These components load even if user never sees them */}
      <HeavyComponent />
      <AnotherHeavyComponent />
    </div>
  );
}

React.lazy() to the Rescue

React.lazy() lets you defer loading components until they're actually needed:

import React, { lazy, Suspense } from 'react';

// Components are now loaded only when rendered
const HeavyComponent = lazy(() => import('./HeavyComponent'));
const AnotherHeavyComponent = lazy(() => import('./AnotherHeavyComponent'));

function App() {
  return (
    <div>
      <Suspense fallback={<div>Loading...</div>}>
        <HeavyComponent />
        <AnotherHeavyComponent />
      </Suspense>
    </div>
  );
}

Route-Based Lazy Loading

Combine with React Router for even better performance:

import React, { lazy, Suspense } from 'react';
import { BrowserRouter, Routes, Route } from 'react-router-dom';

const Home = lazy(() => import('./pages/Home'));
const Dashboard = lazy(() => import('./pages/Dashboard'));
const Settings = lazy(() => import('./pages/Settings'));

function App() {
  return (
    <BrowserRouter>
      <Suspense fallback={<div>Loading...</div>}>
        <Routes>
          <Route path="/" element={<Home />} />
          <Route path="/dashboard" element={<Dashboard />} />
          <Route path="/settings" element={<Settings />} />
        </Routes>
      </Suspense>
    </BrowserRouter>
  );
}

Implement these techniques in your React application today and watch your load times improve dramatically!

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When working with SQL Server, you may often need to count the number of unique values in a specific column. This is useful for analyzing data, detecting duplicates, and understanding dataset distributions.

Using COUNT(DISTINCT column_name)

To count the number of unique values in a column, SQL Server provides the COUNT(DISTINCT column_name) function. Here’s a simple example:

SELECT COUNT(DISTINCT column_name) AS distinct_count
FROM table_name;

This query will return the number of unique values in column_name.

Counting Distinct Values Across Multiple Columns

If you need to count distinct combinations of multiple columns, you can use a subquery:

SELECT COUNT(*) AS distinct_count
FROM (SELECT DISTINCT column1, column2 FROM table_name) AS subquery;

This approach ensures that only unique pairs of column1 and column2 are counted.

Why Use COUNT DISTINCT?

  • Helps in identifying unique entries in a dataset.
  • Useful for reporting and analytics.
  • Efficient way to check for duplicates.

By leveraging COUNT(DISTINCT column_name), you can efficiently analyze your database and extract meaningful insights. Happy querying!

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Storing passwords as plain text is dangerous. Instead, you should hash them using a strong, slow hashing algorithm like BCrypt, which includes built-in salting and resistance to brute-force attacks.

Step 1: Install BCrypt NuGet Package

Before using BCrypt, install the BCrypt.Net-Next package:

dotnet add package BCrypt.Net-Next

or via NuGet Package Manager:

Install-Package BCrypt.Net-Next

Step 2: Hash a Password

Use BCrypt.HashPassword() to securely hash a password before storing it:

using BCrypt.Net;

string password = "mySecurePassword123";
string hashedPassword = BCrypt.HashPassword(password);

Console.WriteLine(hashedPassword); // Output: $2a$12$...

Step 3: Verify a Password

To check a user's login attempt, use BCrypt.Verify():

bool isMatch = BCrypt.Verify("mySecurePassword123", hashedPassword);
Console.WriteLine(isMatch); // Output: True

Ensuring proper hashing should be at the top of your list when it comes to building authentication systems.

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