Parallel LINQ (PLINQ)

Vaibhav • September 11, 2025

As your applications grow and data volumes increase, performance becomes a critical concern. LINQ is expressive and powerful, but by default, it processes data sequentially - one item at a time. What if you could take advantage of multiple CPU cores to process data faster? That’s exactly what Parallel LINQ (PLINQ) offers. PLINQ is a parallel implementation of LINQ that can dramatically improve performance for certain workloads by distributing query execution across multiple threads.

What Is PLINQ?

PLINQ stands for Parallel LINQ. It’s part of the System.Linq namespace and provides parallelized versions of LINQ queries. Instead of processing elements one-by-one, PLINQ splits the input sequence into chunks and processes them concurrently using multiple threads. This can lead to significant speedups - especially for CPU-bound operations on large datasets.

Getting Started with PLINQ

To use PLINQ, simply call AsParallel() on your collection. This converts the sequence into a parallel query. From there, you can use standard LINQ operators like Where, Select, OrderBy, etc.

List numbers = Enumerable.Range(1, 1000000).ToList();

var evenSquares = numbers
    .AsParallel()
    .Where(n => n % 2 == 0)
    .Select(n => n * n)
    .ToList();

This query filters even numbers and computes their squares - in parallel. The result is materialized with ToList(). The performance gain depends on the number of CPU cores and the complexity of the operations.

When to Use PLINQ

PLINQ is best suited for:

• Large datasets (thousands or millions of items)
• CPU-bound operations (e.g., mathematical computations, transformations)
• Independent operations (no shared state or dependencies between items)

Avoid PLINQ for small datasets, I/O-bound operations, or queries with side effects. The overhead of parallelization may outweigh the benefits in those cases.

Controlling Parallelism

PLINQ gives you control over how parallelism is applied. You can use WithDegreeOfParallelism() to specify the number of threads, and WithExecutionMode() to force parallel execution.

var result = numbers
    .AsParallel()
    .WithDegreeOfParallelism(4)
    .WithExecutionMode(ParallelExecutionMode.ForceParallelism)
    .Where(n => n % 2 == 0)
    .Select(n => n * n);

This query uses up to 4 threads and forces parallel execution even if PLINQ thinks sequential might be faster. Use these options carefully - forcing parallelism can hurt performance if misused.

Preserving Order with PLINQ

By default, PLINQ does not preserve the order of elements. This is intentional - removing ordering constraints allows better parallel performance. However, if order matters, you can use AsOrdered().

var orderedResult = numbers
    .AsParallel()
    .AsOrdered()
    .Where(n => n % 2 == 0)
    .Select(n => n * n);

This ensures that the output sequence maintains the original order. Keep in mind that preserving order may reduce parallel efficiency.

Handling Exceptions in PLINQ

Since PLINQ runs queries on multiple threads, exceptions are aggregated into an AggregateException. You need to handle this explicitly.

try
{
    var result = numbers
        .AsParallel()
        .Select(n => 100 / (n % 5)) // May throw DivideByZero
        .ToList();
}
catch (AggregateException ex)
{
    foreach (var inner in ex.InnerExceptions)
    {
        Console.WriteLine(inner.Message);
    }
}

This example catches and prints all exceptions that occurred during parallel execution. Always handle exceptions carefully in PLINQ to avoid silent failures.

Using Cancellation with PLINQ

PLINQ supports cancellation via CancellationToken. This allows you to stop long-running queries based on user input or timeouts.

CancellationTokenSource cts = new CancellationTokenSource();

try
{
    var result = numbers
        .AsParallel()
        .WithCancellation(cts.Token)
        .Select(n => n * n)
        .ToList();
}
catch (OperationCanceledException)
{
    Console.WriteLine("Query was cancelled.");
}

You can call cts.Cancel() from another thread to stop the query. This is useful for responsive applications or time-sensitive operations.

Avoiding Side Effects

PLINQ is designed for pure, side-effect-free operations. Avoid modifying shared state inside your query - it can lead to race conditions, deadlocks, or unpredictable behavior.

// ❌ Avoid this
int total = 0;

var result = numbers
    .AsParallel()
    .Select(n =>
    {
        total += n; // Unsafe
        return n;
    });

Instead, use aggregation methods like Sum() or Aggregate() which are thread-safe and designed for parallel use.

Comparing LINQ and PLINQ

LINQ and PLINQ share the same syntax and operators. The difference lies in execution. LINQ runs sequentially, while PLINQ runs in parallel. Here’s a quick comparison:

// LINQ
var result = numbers.Where(n => n > 100).Select(n => n * 2);

// PLINQ
var result = numbers.AsParallel().Where(n => n > 100).Select(n => n * 2);

Switching to PLINQ is as simple as adding AsParallel(). But always test and measure - parallelism doesn’t guarantee better performance.

Measuring PLINQ Performance

Use Stopwatch or profiling tools to measure the performance of PLINQ queries. Compare execution time with sequential LINQ to determine if parallelism is beneficial.

var sw = Stopwatch.StartNew();

var result = numbers.AsParallel().Select(n => ExpensiveOperation(n)).ToList();

sw.Stop();
Console.WriteLine($"Elapsed: {sw.ElapsedMilliseconds} ms");

This helps you make data-driven decisions about when to use PLINQ. Not all queries benefit from parallelism - measure before optimizing.

Best Practices for PLINQ

To get the most out of PLINQ:

• Use it for CPU-bound, large-scale queries
• Avoid side effects and shared state
• Materialize results with ToList() or ToArray()
• Use AsOrdered() only when necessary
• Handle exceptions and cancellations gracefully
• Profile and test before deploying

Summary

Parallel LINQ (PLINQ) extends LINQ with parallel execution capabilities, allowing you to process large datasets faster by leveraging multiple CPU cores. It’s easy to use - just call AsParallel() - and integrates seamlessly with existing LINQ syntax. PLINQ is ideal for CPU-bound, side-effect-free operations, and supports ordering, cancellation, and exception handling. But it’s not always faster - measure and test before using it in production. By understanding how PLINQ works and applying best practices, you can write high-performance LINQ queries that scale with your data and hardware.

In the next article, we’ll wrap up Chapter 15 with LINQ Best Practices - a collection of tips, patterns, and guidelines to help you write clean, efficient, and maintainable LINQ code.