Large datasets and computationally demanding activities are becoming increasingly prevalent in software applications, thus developers need effective tools to handle data. PLINQ (Parallel LINQ) and LINQ (Language Integrated Query) are two well-liked choices in C#. Although their syntax and functionality are identical, their query execution methods are very different. With the help of real-world examples and performance comparisons, this article examines the main distinctions, applications, and performance factors between LINQ and PLINQ.

What is LINQ?
LINQ (Language Integrated Query) is a feature of C# that enables developers to perform data querying in a syntax integrated into the language. Introduced in .NET Framework 3.5, LINQ provides a consistent method to work with different data sources like collections, databases, XML, and more. It executes queries sequentially, processing each item in turn.

LINQ Example

var numbers = new List<int> { 1, 2, 3, 4, 5 };
var evenNumbers = numbers.Where(n => n % 2 == 0).ToList();

foreach (var number in evenNumbers)
{
    Console.WriteLine(number); // Output: 2, 4
}

LINQ is straightforward to use and works well for small-to-medium-sized datasets or queries that are not computationally intensive.

What is PLINQ?
PLINQ (Parallel LINQ) was introduced with .NET Framework 4.0 and extends LINQ by enabling parallel query execution. Built on the Task Parallel Library (TPL), PLINQ uses multiple CPU cores to process large datasets or computationally expensive operations more efficiently. It partitions data into chunks and executes them concurrently using threads.

PLINQ Example
var numbers = Enumerable.Range(1, 10_000);
var evenNumbers = numbers.AsParallel()
                         .Where(n => n % 2 == 0)
                         .ToList();

Console.WriteLine(evenNumbers.Count); // Output: 5000

The AsParallel() method enables parallel execution of the query, leveraging all available processor cores.

Performance Comparison Between LINQ and PLINQ
To better understand how LINQ and PLINQ differ in performance, let’s process a large dataset and measure the time taken for each.

Example: LINQ vs PLINQ Performance
The following code processes a dataset of numbers from 1 to 5,000,000 and filters prime numbers using both LINQ and PLINQ. We also measure execution time.
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;

class Program
{
    static void Main()
    {
        // Prepare a large dataset
        var largeDataSet = Enumerable.Range(1, 5_000_000).ToList();

        // LINQ benchmark
        var stopwatch = Stopwatch.StartNew();
        var linqPrimes = largeDataSet.Where(IsPrime).ToList();
        stopwatch.Stop();
        Console.WriteLine($"LINQ Time: {stopwatch.ElapsedMilliseconds} ms");
        Console.WriteLine($"LINQ Prime Count: {linqPrimes.Count}");

        // PLINQ benchmark
        stopwatch.Restart();
        var plinqPrimes = largeDataSet.AsParallel().Where(IsPrime).ToList();
        stopwatch.Stop();
        Console.WriteLine($"PLINQ Time: {stopwatch.ElapsedMilliseconds} ms");
        Console.WriteLine($"PLINQ Prime Count: {plinqPrimes.Count}");
    }

    static bool IsPrime(int number)
    {
        if (number <= 1) return false;
        for (int i = 2; i <= Math.Sqrt(number); i++)
        {
            if (number % i == 0) return false;
        }
        return true;
    }
}

Explanation of Benchmark

• Dataset: A large range of numbers (1 to 5,000,000) serves as the input.
• LINQ: The query is processed sequentially, examining each number to determine if it is prime.
• PLINQ: The query runs in parallel, dividing the dataset into chunks for multiple threads to process concurrently.

Expected Output
On a multi-core machine, you might see performance results like:

Ordered vs Unordered Processing in PLINQ
By default, PLINQ processes data in unordered mode to maximize performance. However, if your application requires results to be in the same order as the input dataset, you can enforce order using .AsOrdered().

Example. Using .AsOrdered() in PLINQ
var numbers = Enumerable.Range(1, 10);
var orderedResult = numbers.AsParallel()
                       .AsOrdered()
                       .Where(n => n % 2 == 0)
                       .ToList();
Console.WriteLine(string.Join(", ", orderedResult)); // Output: 2, 4, 6, 8, 10

If maintaining the order doesn’t matter, you can use .AsUnordered() to further optimize performance.

Benchmark. Ordered vs Unordered PLINQ
var numbers = Enumerable.Range(1, 1_000_000).ToList();

var stopwatch = Stopwatch.StartNew();

// Ordered PLINQ
var orderedPrimes = numbers.AsParallel()
                       .AsOrdered()
                       .Where(IsPrime)
                       .ToList();
stopwatch.Stop();
Console.WriteLine($"AsOrdered Time: {stopwatch.ElapsedMilliseconds} ms");

stopwatch.Restart();

// Unordered PLINQ
var unorderedPrimes = numbers.AsParallel()
                         .AsUnordered()
                         .Where(IsPrime)
                         .ToList();
stopwatch.Stop();
Console.WriteLine($"AsUnordered Time: {stopwatch.ElapsedMilliseconds} ms");

Expected Output
AsOrdered Time: 210 ms
AsUnordered Time: 140 ms

Use Cases for LINQ and PLINQ

When to Use LINQ?

• Small datasets where sequential processing is efficient.
• Tasks requiring strict order preservation.
• Easy debugging and simple queries.
• Real-time systems where lower latency matters more than raw throughput.

When to Use PLINQ?

• Large datasets where parallel execution can reduce runtime.
• Computationally intensive tasks, such as processing images or mathematical operations.
• Bulk operations where order doesn’t matter, e.g., statistical analysis of logs.
• Applications running on multi-core machines utilize available CPU resources.

Summary Table of Insights
Key Differences Between LINQ and PLINQ

Conclusion
In C#, LINQ and PLINQ are both great tools for data queries. PLINQ performs best in situations requiring extensive data processing or operations on huge datasets where parallelism may be used, whereas LINQ is appropriate for smaller, simpler datasets.

Depending on whether you need result ordering or efficiency is your first priority, PLINQ offers both ordered and unordered processing options. The optimum method for your use case can be found by benchmarking your query in real-world situations.

By striking a balance between order sensitivity, performance, and application complexity, you may optimize LINQ and PLINQ and write code that is both efficient and maintainable.