5.1 Parallel streams to increase the performance of a time-consuming save file tasks. They look quite similar and it’s easy to get lost when you are not familiar with the API. This Java code will generate 10,000 random employees and save into 10,000 files, each employee save into a file. This approach was used when developing 1.0.0 of parallel-collectors. 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. Currently, JDK IO-based Stream sources (for example BufferedReader.lines()) are mainly geared for sequential use, processing elements one-by-one as they arrive. The tasks which return a result. 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. The example providing its multithreading nature which is given as follows. This approach was also employed when developing 1.0.0 of parallel-collectors. The static factory methods in this class are the starting points for executing tasks. Aggregate operations iterate over and process these substreams in parallel and then combine the results. Streaming CompletableFutures. For normal stream, it takes 27-29 seconds. Also notice the name of threads. CompletableFuture class. This article discusses their difference and helps you understand which is the best choice for you depending on the situation. 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. 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). On basic level, the tasks started by CompletableFuture can be divided in two categories: The tasks which do not return any result. Specifically for our task execution application, if there are 50 tasks we can have better performance, with the following Executor : In parallel stream, Fork and Join framework is used in the background to create multiple threads. Streaming CompletableFutures P.S Tested with i7-7700, 16G RAM, WIndows 10 You can execute streams in serial or in parallel. In these cases, parallel streams are not usually a good choice, but good alternatives are available, for example async-IO and CompletableFuture designs. When a stream executes in parallel, the Java runtime partitions the stream into multiple substreams. Any stream operation without explicitly specified as parallel is treated as a sequential stream. 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. CompletableFuture implements CompletionStage and Future. What if my source is based on IO? When you create a stream, it is always a serial stream unless otherwise specified. 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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