forkjoinpool

问题 I use the ForkJoinPool to execute tasks in parallel. When I look at the logout put of my program it seems that the ForkJoinPool creates a huge amount of workers to execute my tasks (there are log entries that look like this: 05 Apr 2016 11:39:18,678 [ForkJoinPool-2-worker-2493] <message> ). Is there a worker for each tasks created which is then executed according to the number of parallelism I configured in the ForkJoinPool or am I doing something wrong? Here is how I do it: public class

问题 This is a program for Fibonacci using Java 7 ForkJoin . But seems like there is a dead lock. package ForkJoin; import java.time.LocalTime; import java.util.concurrent.ForkJoinPool; import java.util.concurrent.RecursiveTask; import static java.time.temporal.ChronoUnit.MILLIS; class Fibonacci extends RecursiveTask<Integer>{ int num; Fibonacci(int n){ num = n; } @Override protected Integer compute() { if(num <= 1) return num; Fibonacci fib1 = new Fibonacci(num-1); fib1.fork(); Fibonacci fib2 =

来源： https://stackoverflow.com/questions/51650782/forkjoinpool-why-program-is-throwing-outofmemoryerror

来源： https://stackoverflow.com/questions/51650782/forkjoinpool-why-program-is-throwing-outofmemoryerror

来源： https://stackoverflow.com/questions/51650782/forkjoinpool-why-program-is-throwing-outofmemoryerror

来源： https://stackoverflow.com/questions/51650782/forkjoinpool-why-program-is-throwing-outofmemoryerror

问题 In one of my use cases I need to fetch the data from multiple nodes. Each node maintains a range (partition) of data. The goal is to read the data as fast as possible. Constraints are, cardinality of a partition is not known before hand. Using work sharing approach, I could split the partitions into sub-partitions and fetch the data in parallel. One drawback with this approach is, it is possible that one thread could fetch lot of data and take more time while the other thread could finish

问题 In one of my use cases I need to fetch the data from multiple nodes. Each node maintains a range (partition) of data. The goal is to read the data as fast as possible. Constraints are, cardinality of a partition is not known before hand. Using work sharing approach, I could split the partitions into sub-partitions and fetch the data in parallel. One drawback with this approach is, it is possible that one thread could fetch lot of data and take more time while the other thread could finish

问题 This question already has answers here : Why does stream parallel() not use all available threads? (2 answers) Closed yesterday . I want to use my custom ForkJoinPool to have more parallelism with ArrayList.parallelStream() (by default it uses common pool). I do this: List<String> activities = new ArrayList<>(); for (int i = 0; i < 3000; i++) { activities.add(String.valueOf(i)); } ForkJoinPool pool = new ForkJoinPool(10); pool.submit(() -> activities.parallelStream() .map(s -> { try { System

问题 This question already has answers here : Why does stream parallel() not use all available threads? (2 answers) Closed yesterday . I want to use my custom ForkJoinPool to have more parallelism with ArrayList.parallelStream() (by default it uses common pool). I do this: List<String> activities = new ArrayList<>(); for (int i = 0; i < 3000; i++) { activities.add(String.valueOf(i)); } ForkJoinPool pool = new ForkJoinPool(10); pool.submit(() -> activities.parallelStream() .map(s -> { try { System