PLINQ: Implementation, Control, and Best Practices

Building on our understanding of the differences between LINQ and PLINQ, implementing Parallel LINQ requires more than just adding a method call. To truly leverage multi-core processors, developers must understand how to enable parallelism, manage system resources, and handle the lifecycle of a query.

1. Enabling Parallelism with AsParallel()

The gateway to PLINQ is the AsParallel() extension method. It transforms a standard IEnumerable<T> into a parallel query. However, it is important to note that PLINQ is designed for in-memory collections.

While it works seamlessly with objects in memory (LINQ to Objects), it does not directly support IQueryable<T> (LINQ to Entities/SQL). For database queries, the SQL engine handles parallelism; if you must use PLINQ on database results, you must first bring the data into memory using AsEnumerable().

Basic Syntax Example:

2. Controlling Resources: Degree of Parallelism

By default, PLINQ attempts to use all available logical processors. While this maximizes throughput for a single application, it can be problematic on a shared server or a background task where you don't want to "starve" other processes of CPU time.

You can use WithDegreeOfParallelism() to cap the number of concurrent tasks.

Example: Limiting CPU Usage

Note: The value must be between 1 and 512. Limiting this is highly recommended for web servers (ASP.NET Core) to ensure the thread pool isn't exhausted by a single user request.

3. Handling Query Cancellation

Parallel queries often process massive datasets that might take significant time. To keep your application responsive, PLINQ integrates with CancellationTokenSource. If a user cancels an operation or a timeout occurs, PLINQ will stop the worker threads and throw an OperationCanceledException.

Example: Canceling a Long-Running Query

4. Preserving Order in a Parallel World

One of the biggest side effects of parallelism is the loss of order. Because PLINQ breaks the data into chunks and processes them on different threads, the results may return in a "jumbled" sequence. If the original sequence is vital, use AsOrdered().

Comparison Table: Ordering Impact

Summary of Best Practices

1. Don't over-parallelize: Only use PLINQ for CPU-bound tasks with large datasets (>10,000 items).
2. Use WithDegreeOfParallelism on Servers: Protect your system's overall health by limiting core usage in multi-user environments.
3. Always use Try-Catch: Wrap PLINQ in try-catch blocks to handle both OperationCanceledException and AggregateException.
4. Avoid Shared State: Ensure the logic inside your .Select() or .Where() does not modify shared variables, as this will lead to race conditions.