Asynchronous C# LINQ Queries with Parallel and PLINQ

April 2, 2023

5 minutes read

In today’s world, where data is becoming larger and more complex, it’s essential to harness the power of parallel and asynchronous programming to improve the performance and responsiveness of your applications. One of the ways to achieve this in C# is by using Parallel LINQ (PLINQ) and asynchronous LINQ queries. In this article, we will delve into the world of asynchronous C# LINQ queries with Parallel and PLINQ, exploring their benefits and potential pitfalls, as well as providing practical code examples to help you master these powerful techniques.

Understanding Parallelism and Asynchrony in C# LINQ Queries

Before we dive into the code, let’s first understand the difference between parallelism and asynchrony, and how they can be utilized to improve the performance of your C# LINQ queries.

Parallelism

Parallelism is the process of executing multiple tasks or operations simultaneously, often leveraging multiple CPU cores or processors. This can lead to significant performance improvements, especially when dealing with large datasets or computationally intensive tasks. In the context of C# LINQ queries, parallelism can be achieved using the Parallel class or PLINQ.

Asynchrony

Asynchrony, on the other hand, is the process of executing tasks or operations without waiting for them to complete, allowing other tasks to run concurrently. This can improve the responsiveness of your application, as it can continue executing other tasks while waiting for the results of an asynchronous operation. In C# LINQ queries, asynchrony can be achieved using the async and await keywords.

Now that we have a basic understanding of parallelism and asynchrony, let’s explore how to implement them in C# LINQ queries.

Parallel LINQ (PLINQ)

PLINQ is a parallel implementation of LINQ that enables you to execute LINQ queries across multiple CPU cores or processors, potentially improving the performance of your queries. PLINQ is part of the System.Linq.Parallel namespace and can be used by simply calling the AsParallel() extension method on a LINQ query.

Basic PLINQ Query

Here’s a simple example of a PLINQ query:

Loading code snippet...

In this example, we start by creating a sample dataset of numbers from 0 to 1,000,000. We then call the AsParallel() method on the dataset, which enables PLINQ to execute the query across multiple CPU cores. Finally, we use the Where() method to filter out the even numbers and store them in a list.

Handling Exceptions in PLINQ

When working with PLINQ, it’s important to handle exceptions correctly, as they can occur on multiple threads. To handle exceptions, you can use the AggregateException class, which allows you to catch and process multiple exceptions at once. Here’s an example of how to do this:

Loading code snippet...

In this example, we intentionally throw an exception if the number is divisible by 10,000. When the AggregateException is caught, we loop through the InnerExceptions property and output the exception messages.

PLINQ Performance Considerations

While PLINQ can improve the performance of your LINQ queries, it’s important to consider the overhead associated with parallelism. In some cases, the overhead of creating and synchronizing tasks may negate the performance benefits of parallelism. To determine if PLINQ is suitable for your specific use case, it’s recommended to profile your application and analyze the performance impact.

Asynchronous LINQ Queries

In addition to parallelism, asynchrony can also be used to improve the performance and responsiveness of your C# LINQ queries. Asynchronous LINQ queries are particularly useful when querying external data sources, such as databases or web services, as they allow your application to continue executing other tasks while waiting for the query results.

Using Async and Await with LINQ

To create asynchronous LINQ queries, you can use the async and await keywords in conjunction with the Task class. Here’s an example of an asynchronous LINQ query that retrieves data from a web service:

Loading code snippet...

In this example, we first define an asynchronous method GetDataAsync() that fetches data from a web service using the HttpClient class. The await keyword is used to asynchronously wait for the web request to complete, allowing other tasks to run concurrently. We then define another asynchronous method FilterDataAsync() that calls GetDataAsync() and filters the data using a LINQ query.

Combining PLINQ and Asynchronous LINQ Queries

In some cases, you may want to combine both parallelism and asynchrony in your C# LINQ queries to maximize performance and responsiveness. Here’s an example of how to do this:

Loading code snippet...

In this example, we define an asynchronous method ProcessDataAsync() that takes an IEnumerable<int> as input. Inside the method, we use the Task.Run() method to execute a PLINQ query on a separate thread, allowing the main thread to continue executing other tasks. The await keyword is used to asynchronously wait for the PLINQ query to complete.

Conclusion

In this article, we have explored the benefits and challenges of using parallel and asynchronous C# LINQ queries. We have seen how to harness the power of PLINQ and async/await to improve the performance and responsiveness of our applications when working with large datasets or time-consuming operations. By leveraging these techniques, you can create more efficient and responsive applications that are better equipped to handle the demands of modern data processing.

.NET