In parallel stream, Fork and Join framework is used in the background to create multiple threads. This Java code will generate 10,000 random employees and save into 10,000 files, each employee save into a file. CompletableFuture class. 5.1 Parallel streams to increase the performance of a time-consuming save file tasks. When a stream executes in parallel, the Java runtime partitions the stream into multiple substreams. Specifically for our task execution application, if there are 50 tasks we can have better performance, with the following Executor : When you create a stream, it is always a serial stream unless otherwise specified. Also notice the name of threads. Streaming CompletableFutures CompletableFuture has an advantage over parallel streams that we can define our own Executor with the size of thread pool which better suites the need of our application. For normal stream, it takes 27-29 seconds. The tasks which return a result. The static factory methods in this class are the starting points for executing tasks. Sequential stream’s objects are pipelined in a single stream on the same processing system hence it never takes the advantage of the multi-core system even though the underlying system supports parallel execution. Any stream operation without explicitly specified as parallel is treated as a sequential stream. The example providing its multithreading nature which is given as follows. This article discusses their difference and helps you understand which is the best choice for you depending on the situation. Currently, JDK IO-based Stream sources (for example BufferedReader.lines()) are mainly geared for sequential use, processing elements one-by-one as they arrive. On basic level, the tasks started by CompletableFuture can be divided in two categories: The tasks which do not return any result. In these cases, parallel streams are not usually a good choice, but good alternatives are available, for example async-IO and CompletableFuture designs. Java 8 brought us tools like CompletableFuture and Stream API… let’s try to combine them both and create a Stream that returns values from a collection of CompletableFutures as they arrive. They look quite similar and it’s easy to get lost when you are not familiar with the API. Aggregate operations iterate over and process these substreams in parallel and then combine the results. For parallel stream, it takes 7-8 seconds. This clearly shows that in sequential stream, each iteration waits for currently running one to finish, whereas, in parallel stream, eight threads are spawn simultaneously, remaining two, wait for others. Streaming CompletableFutures. P.S Tested with i7-7700, 16G RAM, WIndows 10 What if my source is based on IO? This approach was also employed when developing 1.0.0 of parallel-collectors. Let’s see how we can code the execution of these two parallel threads as a continuation of the previous one: We create (using supplyAsync) a CompletableFuture that gets the safety box number and then combine it with the CompletableFuture that brings the result of the previous chain (thenCombineAsync). CompletableFuture implements CompletionStage and Future. You can execute streams in serial or in parallel. CompletableFuture provides three methods to handle them: handle(), whenComplete(), and exceptionally(). parallel foreach() Works on multithreading concept: The only difference between stream().forEacch() and parrllel foreach() is the multithreading feature given in the parllel forEach().This is way more faster that foreach() and stream.forEach().Like stream().forEach() it also uses lambda symbol to perform functions. 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