The Virtual Thread Stream (VTS) library, contained within the javax0.vtstream
package, leverages the power of virtual threads introduced in Java to provide a high-performance, easy-to-use streaming API that supports parallel processing with minimal overhead.
ThreadedStream implements the Java Stream
interface, providing a seamless integration with the existing stream API. This compatibility allows developers to easily adopt virtual thread-based parallelism in their applications, leveraging the rich set of stream operations such as map
, filter
, sorted
, and more, without having to learn a new API.
-
Parallelism with Virtual Threads: Utilize virtual threads for efficient parallel processing of stream operations, reducing the overhead associated with traditional multi-threading.
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Flexible Stream Operations: Supports a wide range of operations, including map, filter, distinct, limit, skip, and custom operations through the
Command
class. -
Seamless Integration: Easily integrates with existing Java codebases, allowing for the parallel processing of streams with minimal changes to the code.
To create a ThreadedStream
, you can use the static method threaded
provided by the ThreadedStream
class:
Stream<String> sourceStream = Stream.of("apple", "banana", "cherry");
ThreadedStream<String> threadedStream = ThreadedStream.threaded(sourceStream);
This creates a ThreadedStream
that can process elements in parallel.
Operations such as map, filter, and distinct are applied through the Command
subclasses:
ThreadedStream<String> filteredStream = threadedStream.filter(s -> s.startsWith("a"));
ThreadedStream<String> mappedStream = filteredStream.map(String::toUpperCase);
Each operation returns a new ThreadedStream
instance that represents the stream after the operation has been applied.
The Virtual Thread Stream library offers a powerful and flexible way to perform parallel stream processing in Java, making it easier to write efficient and scalable applications. By leveraging virtual threads, it addresses many of the common challenges associated with parallelism, such as overhead and complexity.
Readme entirely generated by ChatGPT4.