ExecutorService is suitable for managing independent tasks, such as HTTP requests or timing tasks, executed through fixed or cache thread pools; ForkJoinPool is suitable for split-merged recursive tasks, using work theft to improve CPU utilization. 1. ExecutorService has flexible control and is suitable for task-independent scenarios; 2. ForkJoinPool is used for partitioning and governance problems, such as big data processing; 3. If the task needs to be disassembled and merged, choose ForkJoinPool; 4. Otherwise, use ExecutorService first, because it is simpler and more intuitive.
If you are writing a multi-threaded Java program, you may hesitate between ExecutorService and ForkJoinPool . They can all handle concurrent tasks, but the applicable scenarios are different. Simply put: if your task can be split into small pieces and executed in parallel, use ForkJoinPool; if you just want to manage a set of threads to execute tasks, ExecutorService is more suitable.
When should I use ExecutorService?
ExecutorService is one of the most basic thread pool implementations in Java concurrency package. It is suitable for performing independent, non-interdependent tasks, such as handling HTTP requests, timing tasks, or batch processing of data.
A common way to use it is to create a fixed-size or cached thread pool:
ExecutorService executor = Executors.newFixedThreadPool(4);
executor.submit(() -> {
// Do something});Its advantages are its simplicity, intuitiveness, flexible control , and you can decide what tasks to submit and how to recycle resources. Moreover, many frameworks (such as Spring) are also processed asynchronously based on it by default.
Suggested scenarios:
- Each task is independent of each other
- No need to split tasks recursively
- Have clear control requirements for task scheduling
What are the advantages of ForkJoinPool?
ForkJoinPool is designed to solve the problem of "dividing and conquer" and is used together with ForkJoinTask or its subclasses (such as RecursiveTask and RecursiveAction ). It uses the "work steal" algorithm internally, and idle threads can "steal" tasks from other threads to execute, improving CPU utilization.
For example, you want to sort a very large array:
class SortTask extends RecursiveAction {
private int[] array;
private int start, end;
public SortTask(int[] array, int start, int end) {
this.array = array;
this.start = start;
this.end = end;
}
protected void compute() {
if (end - start <= THRESHOLD) {
// Small range sorting} else {
int mid = (start end) / 2;
SortTask left = new SortTask(array, start, mid);
SortTask right = new SortTask(array, mid, end);
invokeAll(left, right); // Fork to execute merge(left.get(), right.get()); // Merge result}
}
}This pattern is very suitable for tasks such as image processing, big data analysis, tree structure traversal, etc.
Suggested scenarios:
- Tasks can be disassembled into multiple subtasks
- The subtask ultimately needs to merge the results
- Need to make full use of multi-core CPU resources
The key difference between them
| characteristic | ExecutorService | ForkJoinPool |
|---|---|---|
| Task Model | Simple Runnable/Callable | ForkJoinTask and its subclasses |
| Task Scheduling | FIFO or custom order | Work theft, dynamic allocation |
| Applicable scenarios | Independent task processing | Partition-based task |
| Ease of use | Simple and direct | A little more complex |
| Default number of threads | Configurable | Usually equal to the number of CPU cores |
In addition, ForkJoinPool also supports commonPool() in Java 8, and many parallel stream operations (parallelStream) are run using this pool.
How to choose? It's enough to see these points
- If you just want to open a few threads to execute some tasks that do not interfere with each other → Use
ExecutorService - If your task can be split recursively and finally merge the results → Prioritize
ForkJoinPool - If you are not sure, start with
ExecutorServiceas it is easier to get started - If performance becomes a bottleneck, then evaluate whether it is worth moving to
ForkJoinPool
Basically that's it.
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