Hello friends,
In this article, I will explain to you below details of PLINQ
- What is Parallel LINQ (PLINQ) in C#
- What are the Benefits of PLINQ
- How to Write Parallel LINQ Queries
- What are the methods available in PLINQ
- Performance
- Example
1. What is Parallel LINQ (PLINQ) in C#
Parallel LINQ (PLINQ) is an extension of LINQ (Language Integrated Query) in C# that allows you to write queries that are automatically parallelized and executed on multiple threads.
By leveraging the power of multi-core processors, PLINQ improves query performance.
It simplifies the process of writing parallel code for data-intensive operations, such as filtering, transforming, and aggregating large datasets.
PLINQ automatically partitions the data and processes it concurrently on multiple threads. It uses the Task Parallel Library (TPL) under the hood to manage the parallelization of query operations.
This parallel execution can significantly improve performance, especially when dealing with large datasets.
2. What are the benefits of PLINQ
LINQ provides a convenient way to query data, but PLINQ takes it to the next level by automatically parallelizing the queries. This can lead to significant speed improvements when working with extensive datasets or results
3. What are the methods available in PLINQ
Here's a list of common methods available for configuring the behavior of Parallel LINQ (PLINQ) queries
- Parallel execution for a LINQ query by converting it into a PLINQ query.
- WithDegreeOfParallelism(int degreeOfParallelism): Specifies the maximum number of concurrently executing tasks that will be used by the query.
- WithExecutionMode(ParallelExecutionMode mode): Sets the execution mode of the PLINQ query to either ParallelExecutionMode.ForceParallelism or ParallelExecutionMode.Default.
- WithCancellation(CancellationToken cancellationToken): Specifies a cancellation token to enable cancellation of the PLINQ query.
- WithMergeOptions(ParallelMergeOptions options): Sets the merge options for combining results from parallel partitions (e.g., ParallelMergeOptions.NotBuffered or ParallelMergeOptions.AutoBuffered).
- WithOrdered(): Indicates that the output of the query should maintain the original order, even when parallelized.
- WithDegreeOfParallelism(int degreeOfParallelism): Sets the maximum number of concurrently executing tasks used by the query.
- WithExecutionMode(ParallelExecutionMode mode): Specifies the execution mode of the query (ForceParallelism or Default).
- WithCancellation(CancellationToken cancellationToken): Specifies a cancellation token for enabling query cancellation.
- WithMergeOptions(ParallelMergeOptions options): Sets the merge options for combining results from parallel partitions.
- ForAll(Action<TSource> action): Executes the specified action on each element of the query in parallel.
- AsSequential(): Returns a query that ensures subsequent operations are executed sequentially.
- AsUnordered(): Returns an unordered query that may execute more efficiently by ignoring the order of elements.
- Where<TSource>(Func<TSource, bool> predicate): Filters the elements of the query based on a specified condition.
- Select<TSource, TResult>(Func<TSource, TResult> selector): Projects each element of the query into a new form.
- OrderBy<TSource, TKey>(Func<TSource, TKey> keySelector): Sorts the elements of the query in ascending order based on a specified key.
- OrderByDescending<TSource, TKey>(Func<TSource, TKey> keySelector): Sorts the elements of the query in descending order based on a specified key.
- ThenBy<TSource, TKey>(Func<TSource, TKey> keySelector): Performs a secondary ascending sort on the elements based on a specified key.
- ThenByDescending<TSource, TKey>(Func<TSource, TKey> keySelector): Performs a secondary descending sort on the elements based on a specified key.
- GroupBy<TSource, TKey>(Func<TSource, TKey> keySelector): Groups the elements of the query based on a specified key.
- SelectMany<TSource, TResult>(Func<TSource, IEnumerable<TResult>> selector): Projects each element of the query into a sequence and flattens the resulting sequences into one sequence.
4. Performance of PLINQ queries
PLINQ is most effective when dealing with operations that are time-consuming, such as computations, filtering, or transformations. It may not be beneficial for relatively quick operations, as the overhead of parallelization could outweigh the performance gain.
Although PLINQ can significantly speed up data processing, it's not always the best choice. Make sure to consider factors like data size, hardware, and overhead when deciding whether to use PLINQ.
5. How to write Parallel LINQ Queries
PLINQ seamlessly integrates with the LINQ query syntax. You can use the familiar LINQ keywords (from, where, select, group, join, etc.) to construct your queries. The difference is that PLINQ will automatically parallelize the execution of these queries when appropriate.
