In this lesson, you will learn how to supercharge your standard LINQ queries by transforming them into parallel operations that utilize all available CPU cores.
The Power of Declarative Parallelism
Up until now, we have used LINQ (IEnumerable<T>) to process data sequentially. While this is clean and readable, it processes elements one by one, which can be a bottleneck on modern multi-core processors. PLINQ (Parallel LINQ) allows you to transition from sequential to parallel execution simply by adding one operator: .AsParallel().
By calling .AsParallel(), you signal to the .NET runtime that the subsequent query operations—like Select, Where, or OrderBy—are candidates for parallelization. The PLINQ engine then automatically partitions your data source, distributes the work across multiple threads, and merges the results back together for you.
Putting It Into Practice
Imagine you have a large list of numbers and need to perform a CPU-intensive calculation on each one:
var numbers = Enumerable.Range(1, 1000000);
// Sequential LINQ
var results = numbers.Select(n => ExpensiveCalculation(n)).ToList();
// Parallel LINQ
var parallelResults = numbers
.AsParallel()
.Select(n => ExpensiveCalculation(n))
.ToList();
In the second example, AsParallel() instructs the query to split the one million items into chunks, processing them concurrently. Once every chunk is finished, PLINQ handles the "merge" phase, ensuring your final list contains all the processed results.
When to Use It
It is important to remember that parallelization carries overhead—creating threads and merging results takes time. PLINQ shines when your operations are "CPU-bound" (like complex math, image processing, or heavy string manipulation) and your data set is large enough that the time saved by parallel execution outweighs the overhead of managing the threads. For simple, fast operations on small collections, the overhead might actually make your code slower than a standard sequential query.
Key Takeaway: By calling .AsParallel() on a LINQ query, you declaratively instruct the runtime to partition your data and process it across multiple threads, significantly increasing throughput for compute-intensive tasks.