PLINQ in .NET Core

In the world of .NET Core, parallel processing can significantly enhance the performance and efficiency of data-intensive operations. Enter PLINQ (Parallel LINQ), a powerful feature that combines the ease of LINQ query syntax with the power of parallel execution. In this article, we'll delve into PLINQ, exploring its capabilities and providing code examples to demonstrate its usage.


Understanding PLINQ

PLINQ, an extension to LINQ, allows us to process data in a parallel and concurrent manner, harnessing the power of multiple CPU cores. It automatically partitions data, distributes workloads across threads, and provides built-in load balancing. Here are some key points to grasp about PLINQ:

  1. Parallel Execution: PLINQ enables parallel execution of LINQ queries, leveraging multiple threads and processors. This can greatly improve performance for computationally intensive or data-parallel operations.
  2. Integration with LINQ: PLINQ seamlessly integrates with LINQ, allowing you to write parallel queries using the familiar LINQ query syntax or method syntax. You can leverage existing LINQ knowledge and apply it to parallel scenarios.
  3. Automatic Parallelism: PLINQ handles thread management and parallelization automatically. It determines the optimal degree of parallelism based on available hardware resources and workload characteristics, making it easy to leverage parallelism without explicit thread management.



Basic Usage of PLINQ

Let's explore some basic usage scenarios of PLINQ through code examples:

  1. Parallelizing a LINQ Query:
var numbers = Enumerable.Range(1, 1000);
var evenSquares = numbers.AsParallel()
    .Where(n => n % 2 == 0)
    .Select(n => n * n)
    .ToList();

In this example, we parallelize the LINQ query to find even squares of numbers from 1 to 1000. The AsParallel() extension method converts the query into a parallel query.


Configuring Parallelism

var fruits = new List<string> { "apple", "banana", "cherry", "date" };
var longFruits = fruits.AsParallel()
    .WithDegreeOfParallelism(2)
    .Where(fruit => fruit.Length > 5)
    .ToList();


Here, we configure the degree of parallelism to limit the number of concurrent threads to 2. We use the WithDegreeOfParallelism() method to control the parallelism level.


Advanced Features and Considerations


PLINQ offers additional features and considerations to fine-tune parallel execution:


Order Preservation

var numbers = Enumerable.Range(1, 1000);
var orderedSquares = numbers.AsParallel()
  .AsOrdered()
  .Select(n => n * n)
  .ToList();


The AsOrdered() method preserves the order of results. It ensures that the query's output maintains the original order of the input data.


Handling Exceptions

var numbers = Enumerable.Range(1, 1000);
try
{
    var evenSquares = numbers.AsParallel()
        .Select(n =>
        {
            if (n % 2 == 0)
                return n * n;
            throw new Exception("Invalid number");
        })
        .ToList();
}
catch (AggregateException ex)
{
    // Handle exceptions here
}


Exceptions in PLINQ are aggregated and thrown as an AggregateException. You can catch this exception to handle any errors that occur during parallel execution.


Data Partitioning and Load Balancing

PLINQ automatically partitions data into smaller chunks and distributes the workload across threads. It utilizes a work-stealing algorithm to balance the load among threads, ensuring efficient resource utilization.


LINQ is a powerful addition to .NET Core, offering a simple and efficient way to leverage parallel processing in LINQ queries. By combining the ease of LINQ syntax with automatic parallelism, PLINQ allows developers to achieve significant performance improvements in data-intensive operations. Understanding PLINQ's usage and considering its advanced features and considerations will help you harness the power of parallelism in your .NET Core applications, leading to faster and more efficient data processing.