In Java, the closest equivalent to .NET's LINQ (Language Integrated Query) is the Stream API introduced in Java 8. Both LINQ in .NET and the Stream API in Java provide a functional approach to handling collections, enabling operations like filtering, mapping, reducing, and sorting in a concise and readable manner. However, there are significant differences in how these two approaches leverage hardware acceleration, which can impact performance.

Syntax and Functional Style

LINQ in .NET provides a rich set of query operators that are integrated into the language. You can use LINQ queries with LINQ query syntax (using from, where, select, etc.) or method syntax (using extension methods like .Where(), .Select(), etc.).

Java Stream API uses method chaining in a fluent API style, with operations like .filter(), .map(), .reduce(), .collect(), and so on.

Example of a LINQ query in C#

var result = from x in collection
             where x > 10
             select x * 2;

Example of LINQ method syntax in C#

var result = collection.Where(x => x>10)
                       .Select(x => x*2);

Example of a Stream in Java

List<Integer> result = collection.stream()
                                 .filter(x -> x > 10)
                                 .map(x -> x * 2)
                                 .collect(Collectors.toList());

Lazy Evaluation

Both LINQ and Stream support lazy evaluation (with the exception of certain terminal operations). This means that operations are not performed until the results are actually needed (e.g., when calling .ToList() in LINQ or .collect() in Java Streams). This can lead to more efficient execution, especially with large data sets.

Parallelism

In LINQ, you can use PLINQ (Parallel LINQ) for parallel processing, which can automatically take advantage of multi-core processors to speed up certain operations.

In Java Streams, parallel streams (.parallelStream()) enable parallel processing.

The .NET version has one pitfall compared to Java: There is no guaranteed order in which the statements are executed, unless you use the keyword AsOrdered().

Performance Considerations

Overhead

Both LINQ and Streams introduce a level of abstraction, and performance can sometimes suffer compared to traditional imperative approaches (e.g., using explicit for loops). However, for many scenarios, the difference is minimal, and the trade-offs are usually acceptable for the benefits in code readability and maintainability.

However on the other side, modern LINQ implementations (LINQ ver 7+) make use of hardware acceleration where this is possible via Span and Vectors significant boosting the execution time.

Lazy Evaluation

Both LINQ and Streams optimize execution by using lazy evaluation. This helps in reducing unnecessary computation, making them more efficient when only certain parts of the collection need to be processed.

Hardware acceleration

While both LINQ and Java Streams support parallel execution, the performance benefits vary. LINQ's integration with hardware acceleration on appropriate platforms (both Intel and AMD) gives it a significant edge in scenarios that require high parallel throughput.

Java Streams, in contrast, can take advantage of parallel processing on all multithreaded CPUs of course but beyond that only benefits from hardware acceleration on specific Oracle systems (e.g., SPARC servers running Oracle Solaris), which limits the scope of these performance gains.  Oracle reports that Java Streams can be 3x to 22x faster when running on systems with a DAX coprocessor than on x86, making parallel stream processing significantly more efficient on those platforms.  One question remains: did Oracle perform this benchmark comparison only against traditional calculations or against SIMD-accelerated calculations?

Integration with External Systems

LINQ is often integrated directly into various data providers like databases (via LINQ to SQL or Entity Framework). This allows you to write queries that are executed on the database server rather than in memory, which can significantly improve performance for large datasets.

Java Streams work primarily on in-memory data structures but can be combined with external libraries (e.g., Hibernate, JPA) to interact with databases in a very similar manner.

See also:

Transitioning from C# LINQ to Java Streams where the differences between the functions of the two technologies are explained in full detail.

Streams are similar to LINQ in many ways, but there are still some differences. Because Java only started using streams in 2014, the way in which they apply it to simplify querying sets of data can seem a little bit half-heartedly to a .NET developer (since LINQ was already introduced in 2008) and verbose. And quite a few LINQ methods are simply missing in streams like for example .TakeWhile() and even .Where().

A second example is OfType(). The Java language lacks functions like this, but you can work around them by using a combination of the methods provided for the filter and map functions:

var dataset = new Person[] {
                  new Developer("Jack"), new Developer("Robert"),
                  new ProjectManager("Martin"), new ProjectManager("Leo")};
 
// the .NET example
var subset = dataset.OfType<Developer>().ToList();
  
// the Java example
Collection<Person> subset = dataset.stream()
                              .filter(x -> x instanceof Developer)
                              .map(x -> (Developer)x)
                              .collect(Collectors.toList());